Techniques for behavioral pairing in a task assignment system

ABSTRACT

Techniques for behavioral pairing in a task assignment system are disclosed. In one particular embodiment, the techniques may be realized as a method for behavioral pairing in a task assignment system comprising: determining, by at least one computer processor communicatively coupled to and configured to operate in the task assignment system, a priority for each of a plurality of tasks; determining, by the at least one computer processor, an agent available for assignment to any of the plurality of tasks; and assigning, by the at least one computer processor, a first task of the plurality of tasks to the agent using a task assignment strategy, wherein the first task has a lower-priority than a second task of the plurality of tasks.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/717,724, filed on Dec. 17, 2019, which is a continuation of U.S.patent application Ser. No. 15/837,911, filed Dec. 11, 2017 (now U.S.Pat. No. 10,509,671), each of which is hereby incorporated by referenceherein in its entirety.

FIELD OF THE DISCLOSURE

The present disclosure generally relates to behavioral pairing and, moreparticularly, to techniques for behavioral pairing in a task assignmentsystem.

BACKGROUND OF THE DISCLOSURE

A typical task assignment system algorithmically assigns tasks arrivingat the task assignment center to agents available to handle those tasks.At times, the task assignment system may have agents available andwaiting for assignment to tasks. At other times, the task assignmentcenter may have tasks waiting in one or more queues for an agent tobecome available for assignment.

In some typical task assignment centers, tasks are assigned to agentsordered based on time of arrival, and agents receive tasks ordered basedon the time when those agents became available. This strategy may bereferred to as a “first-in, first-out,” “FIFO,” or “round-robin”strategy. For example, in an “L2” environment, multiple tasks arewaiting in a queue for assignment to an agent. When an agent becomesavailable, the task at the head of the queue would be selected forassignment to the agent.

Some task assignment systems prioritize some types of tasks ahead ofother types of tasks. For example, some tasks may be high-prioritytasks, while other tasks are low-priority tasks. Under a FIFO strategy,high-priority tasks will be assigned ahead of low-priority tasks. Insome situations, some low-priority tasks may have a high average waitingtime while high-priority tasks are handled instead. Moreover, agentsthat might have handled low-priority tasks more efficiently may end upbeing assigned to high-priority tasks instead, leading to suboptimaloverall performance in the task assignment system.

In view of the foregoing, it may be understood that there may be a needfor a system that efficiently optimizes the application of a BP strategyin L2 environments of a task assignment system.

SUMMARY OF THE DISCLOSURE

Techniques for behavioral pairing in a task assignment system aredisclosed. In one particular embodiment, the techniques may be realizedas a method for behavioral pairing in a task assignment systemcomprising determining, by at least one computer processorcommunicatively coupled to and configured to operate in the taskassignment system, a priority for each of a plurality of tasks;determining, by the at least one computer processor, an agent availablefor assignment to any of the plurality of tasks; and assigning, by theat least one computer processor, a first task of the plurality of tasksto the agent using a task assignment strategy, wherein the first taskhas a lower-priority than a second task of the plurality of tasks.

In accordance with other aspects of this particular embodiment, thefirst plurality of tasks may comprise a number of tasks from a front ofa queue of tasks.

In accordance with other aspects of this particular embodiment, thenumber of tasks is greater than one and less than ten.

In accordance with other aspects of this particular embodiment, themethod may further comprise determining, by the at least one computerprocessor, an optimal degree of choice for the task assignment strategy,and determining, by the at least one computer processor, the number oftasks based on the optimal degree of choice.

In accordance with other aspects of this particular embodiment, thenumber of tasks may be proportional to a size of the queue of tasks.

In accordance with other aspects of this particular embodiment, thenumber of tasks may be proportional to relative numbers of tasks ofdifferent priorities.

In accordance with other aspects of this particular embodiment, themethod may further comprise determining, by the at least one computerprocessor, that the first task of the plurality of tasks has exceeded arelevant service level agreement.

In accordance with other aspects of this particular embodiment, theservice level agreement may be a function of an estimated wait time forthe first task.

In accordance with other aspects of this particular embodiment, thefirst plurality of tasks may comprise a number of tasks from a front ofa queue of tasks, and wherein the service level agreement may be afunction of the number of tasks.

In accordance with other aspects of this particular embodiment, at leastone of the plurality of tasks may be a virtual task.

In accordance with other aspects of this particular embodiment, the taskassignment strategy may be a behavioral pairing strategy.

In another particular embodiment, the techniques may be realized as asystem for behavioral pairing in a task assignment system comprising atleast one computer processor communicatively coupled to and configuredto operate in the task assignment system, wherein the at least onecomputer processor is further configured to perform the steps in theabove-described method.

In another particular embodiment, the techniques may be realized as anarticle of manufacture for behavioral pairing in a task assignmentsystem comprising a non-transitory processor readable medium andinstructions stored on the medium, wherein the instructions areconfigured to be readable from the medium by at least one computerprocessor communicatively coupled to and configured to operate in thetask assignment system and thereby cause the at least one computerprocessor to operate so as to perform the steps in the above-describedmethod.

The present disclosure will now be described in more detail withreference to particular embodiments thereof as shown in the accompanyingdrawings. While the present disclosure is described below with referenceto particular embodiments, it should be understood that the presentdisclosure is not limited thereto. Those of ordinary skill in the arthaving access to the teachings herein will recognize additionalimplementations, modifications, and embodiments, as well as other fieldsof use, which are within the scope of the present disclosure asdescribed herein, and with respect to which the present disclosure maybe of significant utility.

BRIEF DESCRIPTION OF THE DRAWINGS

To facilitate a fuller understanding of the present disclosure,reference is now made to the accompanying drawings, in which likeelements are referenced with like numerals. These drawings should not beconstrued as limiting the present disclosure, but are intended to beillustrative only.

FIG. 1 shows a block diagram of a task assignment system according toembodiments of the present disclosure.

FIG. 2 shows a flow diagram of a task assignment method according toembodiments of the present disclosure.

DETAILED DESCRIPTION

A typical task assignment system algorithmically assigns tasks arrivingat the task assignment center to agents available to handle those tasks.At times, the task assignment system may have agents available andwaiting for assignment to tasks. At other times, the task assignmentcenter may have tasks waiting in one or more queues for an agent tobecome available for assignment.

In some typical task assignment centers, tasks are assigned to agentsordered based on time of arrival, and agents receive tasks ordered basedon the time when those agents became available. This strategy may bereferred to as a “first-in, first-out,” “FIFO,” or “round-robin”strategy. For example, in an “L2” environment, multiple tasks arewaiting in a queue for assignment to an agent. When an agent becomesavailable, the task at the head of the queue would be selected forassignment to the agent.

Some task assignment systems prioritize some types of tasks ahead ofother types of tasks. For example, some tasks may be high-prioritytasks, while other tasks are low-priority tasks. Under a FIFO strategy,high-priority tasks will be assigned ahead of low-priority tasks. Insome situations, some low-priority tasks may have a high average waitingtime while high-priority tasks are handled instead. Moreover, agentsthat might have handled low-priority tasks more efficiently may end upbeing assigned to high-priority tasks instead, leading to suboptimaloverall performance in the task assignment system.

In view of the foregoing, it may be understood that there may be a needfor a system that efficiently optimizes the application of a BP strategyin L2 environments of a task assignment system.

FIG. 1 shows a block diagram of a task assignment system 100 accordingto embodiments of the present disclosure. The description hereindescribes network elements, computers, and/or components of a system andmethod for benchmarking pairing strategies in a task assignment systemthat may include one or more modules. As used herein, the term “module”may be understood to refer to computing software, firmware, hardware,and/or various combinations thereof. Modules, however, are not to beinterpreted as software which is not implemented on hardware, firmware,or recorded on a non-transitory processor readable recordable storagemedium (i.e., modules are not software per se). It is noted that themodules are exemplary. The modules may be combined, integrated,separated, and/or duplicated to support various applications. Also, afunction described herein as being performed at a particular module maybe performed at one or more other modules and/or by one or more otherdevices instead of or in addition to the function performed at theparticular module. Further, the modules may be implemented acrossmultiple devices and/or other components local or remote to one another.Additionally, the modules may be moved from one device and added toanother device, and/or may be included in both devices.

As shown in FIG. 1, the task assignment system 100 may include a taskassignment module 110. The task assignment system 100 may include aswitch or other type of routing hardware and software for helping toassign tasks among various agents, including queuing or switchingcomponents or other Internet-, cloud-, or network-based hardware orsoftware solutions.

The task assignment module 110 may receive incoming tasks. In theexample of FIG. 1, the task assignment system 100 receives m tasks overa given period, tasks 130A-130 m. Each of the m tasks may be assigned toan agent of the task assignment system 100 for servicing or other typesof task processing. In the example of FIG. 1, n agents are availableduring the given period, agents 120A-120 n. m and n may be arbitrarilylarge finite integers greater than or equal to one. In a real-world taskassignment system, such as a contact center, there may be dozens,hundreds, etc. of agents logged into the contact center to interact withcontacts during a shift, and the contact center may receive dozens,hundreds, thousands, etc. of contacts (e.g., calls) during the shift.

In some embodiments, a task assignment strategy module 140 may becommunicatively coupled to and/or configured to operate in the taskassignment system 100. The task assignment strategy module 140 mayimplement one or more task assignment strategies (or “pairingstrategies”) for assigning individual tasks to individual agents (e.g.,pairing contacts with contact center agents).

A variety of different task assignment strategies may be devised andimplemented by the task assignment strategy module 140. In someembodiments, a first-in/first-out (“FIFO”) strategy may be implementedin which, for example, the longest-waiting agent receives the nextavailable task (in L1 environments) or the longest-waiting task isassigned to the next available task (in L2 environments). Other FIFO andFIFO-like strategies may make assignments without relying on informationspecific to individual tasks or individual agents.

In other embodiments, a performance-based routing (PBR) strategy may beused for prioritizing higher-performing agents for task assignment maybe implemented. Under PBR, for example, the highest-performing agentamong available agents receives the next available task. Other PBR andPBR-like strategies may make assignments using information aboutspecific agents but without necessarily relying on information aboutspecific tasks or agents.

In yet other embodiments, a behavioral pairing (BP) strategy may be usedfor optimally assigning tasks to agents using information about bothspecific tasks and specific agents. Various BP strategies may be used,such as a diagonal model BP strategy or a network flow BP strategy.These task assignment strategies and others are described in detail forthe contact center context in, e.g., U.S. Pat. No. 9,300,802 and U.S.patent application Ser. No. 15/582,223, which are hereby incorporated byreference herein.

In some embodiments, a historical assignment module 150 may becommunicatively coupled to and/or configured to operate in the taskassignment system 100 via other modules such as the task assignmentmodule 110 and/or the task assignment strategy module 140. Thehistorical assignment module 150 may be responsible for variousfunctions such as monitoring, storing, retrieving, and/or outputtinginformation about agent task assignments that have already been made.For example, the historical assignment module 150 may monitor the taskassignment module 110 to collect information about task assignments in agiven period. Each record of a historical task assignment may includeinformation such as an agent identifier, a task or task type identifier,outcome information, or a pairing strategy identifier (i.e., anidentifier indicating whether a task assignment was made using a BPpairing strategy or some other pairing strategy such as a FIFO or PBRpairing strategy).

In some embodiments and for some contexts, additional information may bestored. For example, in a call center context, the historical assignmentmodule 150 may also store information about the time a call started, thetime a call ended, the phone number dialed, and the caller's phonenumber. For another example, in a dispatch center (e.g., “truck roll”)context, the historical assignment module 150 may also store informationabout the time a driver (i.e., field agent) departs from the dispatchcenter, the route recommended, the route taken, the estimated traveltime, the actual travel time, the amount of time spent at the customersite handling the customer's task, etc.

In some embodiments, the historical assignment module 150 may generate apairing model or similar computer processor-generate model based on aset of historical assignments for a period of time (e.g., the past week,the past month, the past year, etc.), which may be used by the taskassignment strategy module 140 to make task assignment recommendationsor instructions to the task assignment module 110. In other embodiments,the historical assignment module 150 may send historical assignmentinformation to another module such as the task assignment strategymodule 140 or the benchmarking module 160.

In some embodiments, a benchmarking module 160 may be communicativelycoupled to and/or configured to operate in the task assignment system100 via other modules such as the task assignment module 110 and/or thehistorical assignment module 150. The benchmarking module 160 maybenchmark the relative performance of two or more pairing strategies(e.g., FIFO, PBR, BP, etc.) using historical assignment information,which may be received from, for example, the historical assignmentmodule 150. In some embodiments, the benchmarking module 160 may performother functions, such as establishing a benchmarking schedule forcycling among various pairing strategies, tracking cohorts (e.g., baseand measurement groups of historical assignments), etc. The techniquesfor benchmarking and other functionality performed by the benchmarkingmodule 160 for various task assignment strategies and various contextsare described in later sections throughout the present disclosure.Benchmarking is described in detail for the contact center context in,e.g., U.S. Pat. No. 9,712,676, which is hereby incorporated by referenceherein.

In some embodiments, the benchmarking module 160 may output or otherwisereport or use the relative performance measurements. The relativeperformance measurements may be used to assess the quality of the taskassignment strategy to determine, for example, whether a different taskassignment strategy (or a different pairing model) should be used, or tomeasure the overall performance (or performance gain) that was achievedwithin the task assignment system 100 while it was optimized orotherwise configured to use one task assignment strategy instead ofanother.

In some task assignment systems, a relatively large number of tasks canbuild up in a queue while waiting for assignment to agents as theybecome available. For this highly simplified example, there are ninetasks waiting in queue. Three of the tasks are high-priority tasks: H1,H2, and H3; and six of the tasks are low-priority tasks: L1, L2, L3, L4,L5, and L6.

In some task assignment systems, the tasks of different priorities maybe organized (within the system, or at least conceptually) in differentpriority queues:

High-Priority Queue: H1, H2, H3

Low-Priority Queue: L1, L2, L3, L4, L5, L6

In this example, each priority queue is chronologically orderedaccording to the arrival time for each task (e.g., contact or caller ina contact center system). H1 is the longest-waiting high-priority task,H3 is the shortest-waiting high-priority task, L1 is the longest-waitinglow-priority task, L6 is the shortest-waiting low-priority task, etc. Insome embodiments, one or more of the tasks may be a “virtual task.” Forexample, in a call center context, a caller may request a callback anddisconnect from the call center, but the caller's position and prioritylevel is maintained in the queue.

In other task assignment systems, the tasks of different priorities maybe intermingled (within the system, or at least conceptually) in achronologically ordered queue, except that higher-priority tasks may beinserted in the queue ahead of lower-priority tasks:

Queue: H1, H2, H3, L1, L2, L3, L4, L5, L6

In this example, even if L1 is the longest-waiting task among all ninetasks, the three high-priority tasks that arrived later in time havebeen inserted into the queue ahead of L1.

A typical FIFO strategy may operate by assigning all of thehigh-priority tasks prior to assigning any of the low-priority tasks,allowing low-priority tasks to wait in the queue indefinitely, even asagents become available that may be able to handle lower-priority tasksmore efficiently than higher-priority tasks. This shortcoming may beespecially pernicious if higher-priority contacts continue arriving atthe task assignment system.

In some task assignment systems, a service level agreement (SLA) may bein place that puts limits on how long any one task should be expected towait for assignment. Some examples of SLAs include a fixed time (e.g.,10 seconds, 30 seconds, 3 minutes, etc.); an estimated wait time (EWT)plus some fixed time (e.g., an EWT of 1 min. 45 sec. plus 30 seconds);and a multiplier of EWT (e.g., 150% of EWT, or 1.2*EWT).

In these task assignment systems, a FIFO strategy may eventually assignsome lower-priority tasks if the SLA is exceeded for that task(sometimes referred to a “blown SLA”). Nevertheless, low-priority tasksmay still end up waiting in the queue for longer than average expectedwait time, and agent assignments may still be made inefficiently.

In some embodiments, a more effective and efficient task assignmentstrategy is a BP strategy. Under a BP strategy, as many as all ninetasks may be considered for assignment when an agent becomes available.The BP strategy may still take the priority level of each task intoaccount, but it may ultimately prefer to assign a lower-priority taskahead of a higher-priority task if information about the task and theavailable agent indicate that such a pairing is optimal for performanceof the task assignment system and achieving a desired target taskutilization or rate of assignment.

The extent to which a BP strategy may account for priority level is aspectrum. On one extreme end of the spectrum, a BP strategy may considerall tasks in queue (or all tasks in all priority queues), givingrelatively little to no weight to each tasks' priority level:

Queue: T1, T2, T3, T4, T5, T6, T7, T8, T9

In this example, the BP strategy may be able to make efficient, optimaltask assignments. However, one possible consequence of this strategy isthat some high-priority tasks may end up waiting much longer than theywould under a FIFO strategy as lower-priority tasks are assigned first

Near the other end of the spectrum, a BP strategy may consider all tasksin queue for the highest-priority level:

High-Priority Queue: H1, H2, H3

In this example, the BP strategy may still be able to make moreefficient, optimal task assignments than the FIFO strategy. Under theFIFO strategy, the tasks would be assigned in queue order: first H1,then H2, and finally H3, regardless of which agent becomes available,whereas the BP strategy would consider information about the three tasksand the agent to select the more efficient pairing, even though theassigned high-priority task may not be the longest-waiting high-prioritytask. However, one possible consequence of this strategy is thatlow-priority tasks may end up waiting just as long as they would underthe FIFO strategy, and opportunities to pair agents with low-prioritytasks efficiently would be missed.

In some embodiments, a hybrid approach may be used that gives somedeference to task prioritization and waiting time while also timelyhandling at least some of the longer-waiting lower-priority tasks. Someof these embodiments may be referred to as “Front-N” or “Head-N” becauseit considers the first N tasks in a prioritized queue.

For example, if N=6, such a BP strategy will select among the first sixtasks in queue:

Queue: H1, H2, H3, L1, L2, L3, L4, L5, L6

In this example, when an agent become available, the BP strategy mayassign any of the three high-priority tasks or any of the threelongest-waiting low-priority tasks.

In some embodiments, N may be a predetermined and/or fixed value. Inother embodiments, N may be dynamically determined for each pairing. Forexample, the BP strategy may determine a size for N that represents anoptimal amount or degree of choice (e.g., 3, 6, 10, 20, etc.). Foranother example, N may be a function of the number of tasks waiting inthe queue (e.g., one-quarter, -third, -half, etc. of the number of tasksin the queue). For another example, N may be a function of the relativenumber tasks at different priority levels.

For another example, the BP strategy may consider up to i calls for i≤Nif it encounters an i-th call for which the SLA has already been blown.In this example, if L1 has already been waiting for longer than the SLAexpects, the BP strategy may consider H1, H2, H3, and L1—disregarding L2and L3 because it will prefer to pair the longer-waiting L1 beforepairing L2 or L3.

In some embodiments, the BP strategy may use a SLA based on tracking howmany times an individual task has been up for selection (i.e., how manytimes a task has appeared in the Front-N tasks):

1. H1(1), H2(1), H3(1), L1(1), L2(1), L3(1)=>H3 selected

2. H1(2), H2(2), L1(2), L2(2), L3(2), L4(1)=>L2 selected

3. H1(3), H2(3), L1(3), L3(3), L4(3), L5(1)=>H1 selected

4. H2(4), L1(4), L3(4), L5(2), L6(1)

If the SLA is based on whether a task has appeared in the Front-6 morethan three times, there are now three tasks with blown SLAs by thefourth assignment: H2, L1, and L3 have now appeared for a fourth time.In these embodiments, the BP strategy may preferably pair these threetasks ahead of other tasks that have appeared in the Front-6 only threeor fewer times (i.e., L5 and L6).

In some embodiments, the SLA based on Front-N may be a function of N.For example, a task may appear in the Front-N up to ½ N, 2N, 5N, etc.before the SLA is blown.

This type of SLA may be especially useful in real-world scenarios inwhich higher-priority tasks continue to arrive at the queue and wouldotherwise be assigned ahead of longer-waiting lower-priority tasks thathave already appeared in the Front-N more than the Front-N SLA wouldnormally expect or allow.

In some embodiments, individual tasks or types of tasks may havedifferent SLAs from other tasks or other types of tasks. The differentSLAs may be based on any of the techniques described above, such astime-based SLAs or SLAs based on the number of times an individual taskhas been included in the Front-N or otherwise evaluated. For example,the first task in the queue may have a SLA of 2N, whereas the secondtask in the queue may have a SLA of 3N. The determination of which SLAan individual task has may be based on information about the task,information about the available agent or agents, or both.

In some embodiments, the SLA for a task may be dynamic, changing as theamount of waiting time increases or the number of times the task hasbeen evaluated in the Front-N increases.

FIG. 2 shows a task assignment method 200 according to embodiments ofthe present disclosure.

Task assignment method 200 may begin at block 210. At block 210, anumber of tasks for a size of a plurality of tasks may be determined. Insome embodiments, the number of tasks for the size of the plurality oftasks may be equivalent to a size of a queue of tasks. For example, in acontact center context, if twenty contacts are waiting in a queue forconnection to an agent, the plurality of tasks would include all twentycontacts from the queue. In other embodiments, the number of tasks maybe a fixed or predetermined number of tasks take from the front or headof the queue. For example, if the number of tasks is ten, the pluralityof tasks may include the first ten tasks (e.g., contacts) from the queueof size twenty. In other embodiments, the number of tasks may bedynamically determined according to any of the techniques describedabove, such as a function (e.g., fraction, percentage, proportion) ofthe size of the queue, a function of a relative number of tasks fordifferent priority levels, a function of a degree of choice for abehavioral pairing strategy, etc. In some embodiments, this number oftasks may be referred to as “N” and the plurality of tasks may bereferred to as the “Front-N” plurality of tasks.

Task assignment method 200 may proceed to block 220. At block 220, apriority may be determined for each of the plurality of tasks (e.g., theFront-N tasks). For example, a first portion of the plurality of tasksmay be designated as “high priority,” and a second portion of theplurality of tasks may be designated as “low priority.” In someembodiments, there may be an arbitrarily large number of differentpriorities and identifiers for priorities. In some embodiments, the taskassignment system may maintain separate queues of tasks for eachpriority. In other embodiments, the task assignment system may maintaina single queue of tasks ordered first by priority and, in some cases,second by order of arrival time or another chronological ordering. Inthese embodiments, task assignment method 200 may consider all tasks orthe Front-N tasks regardless of whether the tasks are maintained in asingle prioritized queue or multiple priority queues.

Task assignment method 200 may proceed to block 230. In someembodiments, whether a SLA has been exceeded for at least one task ofthe plurality of tasks may be determined. In some embodiments, the taskassignment strategy or the task assignment system will assign an agentto a task that has exceeded its SLA (e.g., the longest-waiting task withan exceeded or blown SLA). In various embodiments, the SLA may bedefined or otherwise determined according to the any of the techniquesdescribed above, such as a fixed time, a function of EWT, or a functionof the number of times a given task has been available for assignment inthe Front-N. In other embodiments, there may be no SLA relevant to thetask assignment strategy, and the task assignment method 200 may proceedwithout determining or otherwise checking for any exceeded SLAs.

Task assignment method 200 may proceed to block 240. At block 240, anagent may be determined that is available for assignment to any of theplurality of tasks. For example, in L2 environments, an agent becomesavailable for assignment. In other environments, such as L3environments, multiple agents may be available for assignment.

Task assignment method 200 may proceed to block 250. At block 250, atask of the plurality of tasks may be assigned to the agent using thetask assignment strategy. For example, if the task assignment strategyis a BP strategy, the BP strategy may consider information about each ofthe plurality of tasks and information about the agent to determinewhich task assignment is expected to optimize overall performance of thetask assignment system. In some instances, the optimal assignment may bethe longest-waiting, highest-priority task, as would be the case for aFIFO or PBR strategy. However, in other instances, the optimalassignment may be a longer-waiting and/or lower-priority task. Even ifthese instances, a lower expected performance for the instant pairingmay be expected to lead to a higher overall performance of the taskassignment system while also, in some embodiments, achieving a balancedor otherwise targeted task utilization (e.g., normalizing or balancingaverage waiting time for all tasks, or balancing average waiting timefor all tasks within the same priority level).

In some embodiments, the task assignment strategy or the task assignmentsystem may prioritize assigning a task with an exceeded SLA (such as alongest-waiting and/or highest-priority task with an exceeded SLA) ifthere is one.

In some embodiments, the task assignment system may cycle among multipletask assignment strategies (e.g., cycling between a BP strategy and FIFOor a PBR strategy). In some of these embodiments, the task assignmentsystem may benchmark the relative performance of the multiple taskassignment strategies.

After assigning the task to the agent, ask assignment method 200 mayend.

At this point it should be noted that TK in accordance with the presentdisclosure as described above may involve the processing of input dataand the generation of output data to some extent. This input dataprocessing and output data generation may be implemented in hardware orsoftware. For example, specific electronic components may be employed ina behavioral pairing module or similar or related circuitry forimplementing the functions associated with TK in accordance with thepresent disclosure as described above. Alternatively, one or moreprocessors operating in accordance with instructions may implement thefunctions associated with TK in accordance with the present disclosureas described above. If such is the case, it is within the scope of thepresent disclosure that such instructions may be stored on one or morenon-transitory processor readable storage media (e.g., a magnetic diskor other storage medium), or transmitted to one or more processors viaone or more signals embodied in one or more carrier waves.

The present disclosure is not to be limited in scope by the specificembodiments described herein. Indeed, other various embodiments of andmodifications to the present disclosure, in addition to those describedherein, will be apparent to those of ordinary skill in the art from theforegoing description and accompanying drawings. Thus, such otherembodiments and modifications are intended to fall within the scope ofthe present disclosure. Further, although the present disclosure hasbeen described herein in the context of at least one particularimplementation in at least one particular environment for at least oneparticular purpose, those of ordinary skill in the art will recognizethat its usefulness is not limited thereto and that the presentdisclosure may be beneficially implemented in any number of environmentsfor any number of purposes. Accordingly, the claims set forth belowshould be construed in view of the full breadth and spirit of thepresent disclosure as described herein.

1. A method comprising: determining, by at least one computer processorcommunicatively coupled to and configured to operate in a contact centersystem, a set of contacts; determining, by the at least one computerprocessor, a number N; filtering, by the at least one computerprocessor, the set of contacts to provide a plurality of contacts,wherein the plurality of contacts comprises N contacts, wherein thefiltering is based on a wait time of each contact of the plurality ofcontacts; selecting, by the at least one computer processor, a contactof the plurality of contacts for pairing to an available agent based ona pairing strategy; wherein N is inversely related to a likelihood ofthe pairing strategy selecting a longer-waiting contact of the pluralityof contacts over a shorter-waiting contact of the plurality of contacts.2. The method of claim 1, wherein the filtering further comprisesadjusting, by the at least one computer processor, the number N, whereinincreasing N during the filtering decreases a likelihood of the pairingstrategy selecting a longest-waiting contact of the plurality ofcontacts.
 3. The method of claim 1, wherein the selecting furthercomprises selecting, by the at least one computer processor, a lowerpriority contact while the plurality of contacts comprises an availablehigher priority contact.
 4. The method of claim 1, wherein N comprisesone of: 3, 6, 10, and
 20. 5. The method of claim 1, wherein N comprisesat least
 10. 6. The method of claim 1, wherein the plurality of contactscomprises at least one contact associated with a lower priority and atleast one contact associated with a higher priority.
 7. The method ofclaim 1, wherein N is based on a total number of contacts in the set ofcontacts.
 8. A system comprising: at least one computer processorcommunicatively coupled to and configured to operate in a contact centersystem, wherein the at least one computer processor is furtherconfigured to: determine a set of contacts; determine a number N; filterthe set of contacts to provide a plurality of contacts, wherein theplurality of contacts comprises N contacts, wherein the filtering isbased on a wait time of each contact of the plurality of contacts;select a contact of the plurality of contacts for pairing to anavailable agent based on a pairing strategy; wherein N is inverselyrelated to a likelihood of the pairing strategy selecting alonger-waiting contact of the plurality of contacts over ashorter-waiting contact of the plurality of contacts.
 9. The system ofclaim 8, wherein the at least one computer processor is furtherconfigured to filter by adjusting the number N, wherein increasing Nduring the filtering decreases a likelihood of the pairing strategyselecting a longest-waiting contact of the plurality of contacts. 10.The system of claim 8, wherein the at least one computer processor isfurther configured to select by selecting a lower priority contact whilethe plurality of contacts comprises an available higher prioritycontact.
 11. The system of claim 8, wherein N comprises one of: 3, 6,10, and
 20. 12. The system of claim 8, wherein N comprises at least 10.13. The system of claim 8, wherein the plurality of contacts comprisesat least one contact associated with a lower priority and at least onecontact associated with a higher priority.
 14. The system of claim 8,wherein N is based on a total number of contacts in the set of contacts.15. An article of manufacture comprising: a non-transitory computerprocessor readable medium; and instructions stored on the medium;wherein the instructions are configured to be readable from the mediumby at least one computer processor communicatively coupled to andconfigured to operate in a contact center system and thereby cause theat least one computer processor to operate so as to: determine a set ofcontacts; determine a number N; filter the set of contacts to provide aplurality of contacts, wherein the plurality of contacts comprises Ncontacts, wherein the filtering is based on a wait time of each contactof the plurality of contacts; select a contact of the plurality ofcontacts for pairing to an available agent based on a pairing strategy;wherein N is inversely related to a likelihood of the pairing strategyselecting a longer-waiting contact of the plurality of contacts over ashorter-waiting contact of the plurality of contacts.
 16. The article ofmanufacture of claim 15, wherein the at least one computer processor isfurther caused to operate so as to filter by adjusting the number N,wherein increasing N during the filtering decreases a likelihood of thepairing strategy selecting a longest-waiting contact of the plurality ofcontacts.
 17. The article of manufacture of claim 15, wherein the atleast one computer processor is further caused to operate so as toselect by selecting a lower priority contact while the plurality ofcontacts comprises an available higher priority contact.
 18. The articleof manufacture of claim 15, wherein N comprises one of: 3, 6, 10, and20.
 19. The article of manufacture of claim 15, wherein N comprises atleast
 10. 20. The article of manufacture of claim 15, wherein theplurality of contacts comprises at least one contact associated with alower priority and at least one contact associated with a higherpriority.